






"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Dict      =  """Wav2Vec2FeatureExtractor"""
					lowercase_    :				Tuple      =  """AutoTokenizer"""
					def __init__(							self							:	Tuple     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	List[Any]   )	->       Union[str, Any]:
											super().__init__(UpperCAmelCase     , UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			self.feature_extractor
											lowerCAmelCase   :Optional[Any]						=			False
					@classmethod
					def        UpperCAmelCase__ (							cls							:	Dict     , UpperCAmelCase							:	Dict     , **UpperCAmelCase							:	Union[str, Any]   )	->       List[Any]:
											try:
																	return super().from_pretrained(UpperCAmelCase     , **UpperCAmelCase   )
											except OSError:
																	warnings.warn(
																	    f"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
																	    ' include a `tokenizer_class` attribute is deprecated and will be '
																	    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
																	    ' attribute to either your `config.json` or `tokenizer_config.json` '
																	    'file to suppress this warning: '     , UpperCAmelCase     , )

																	lowerCAmelCase   :str						=			WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase     , **UpperCAmelCase   )
																	lowerCAmelCase   :Dict						=			WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase     , **UpperCAmelCase   )

																	return cls(feature_extractor=UpperCAmelCase     , tokenizer=UpperCAmelCase   )
					def __call__(							self							:	Optional[Any]     , *UpperCAmelCase							:	Union[str, Any]     , **UpperCAmelCase							:	Dict   )	->       Dict:
											# For backward compatibility
											if self._in_target_context_manager:
																	return self.current_processor(*UpperCAmelCase     , **UpperCAmelCase   )

											if "raw_speech" in kwargs:
																	warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'   )
																	lowerCAmelCase   :List[str]						=			kwargs.pop('raw_speech'   )
											else:
																	lowerCAmelCase   :Optional[int]						=			kwargs.pop('audio'     , UpperCAmelCase   )
											lowerCAmelCase   :int						=			kwargs.pop('sampling_rate'     , UpperCAmelCase   )
											lowerCAmelCase   :Optional[int]						=			kwargs.pop('text'     , UpperCAmelCase   )
											if len(UpperCAmelCase   ) > 0:
																	lowerCAmelCase   :Optional[int]						=			args[0]
																	lowerCAmelCase   :Optional[Any]						=			args[1:]

											if audio is None and text is None:
																	raise ValueError('You need to specify either an `audio` or `text` input to process.'   )

											if audio is not None:
																	lowerCAmelCase   :Dict						=			self.feature_extractor(UpperCAmelCase     , *UpperCAmelCase     , sampling_rate=UpperCAmelCase     , **UpperCAmelCase   )
											if text is not None:
																	lowerCAmelCase   :Dict						=			self.tokenizer(UpperCAmelCase     , **UpperCAmelCase   )

											if text is None:
																	return inputs
											elif audio is None:
																	return encodings
											else:
																	lowerCAmelCase   :str						=			encodings['input_ids']
																	return inputs
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , *UpperCAmelCase							:	Optional[Any]     , **UpperCAmelCase							:	Dict   )	->       Union[str, Any]:
											# For backward compatibility
											if self._in_target_context_manager:
																	return self.current_processor.pad(*UpperCAmelCase     , **UpperCAmelCase   )

											lowerCAmelCase   :List[Any]						=			kwargs.pop('input_features'     , UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			kwargs.pop('labels'     , UpperCAmelCase   )
											if len(UpperCAmelCase   ) > 0:
																	lowerCAmelCase   :Tuple						=			args[0]
																	lowerCAmelCase   :Optional[Any]						=			args[1:]

											if input_features is not None:
																	lowerCAmelCase   :Optional[Any]						=			self.feature_extractor.pad(UpperCAmelCase     , *UpperCAmelCase     , **UpperCAmelCase   )
											if labels is not None:
																	lowerCAmelCase   :Optional[int]						=			self.tokenizer.pad(UpperCAmelCase     , **UpperCAmelCase   )

											if labels is None:
																	return input_features
											elif input_features is None:
																	return labels
											else:
																	lowerCAmelCase   :List[Any]						=			labels['input_ids']
																	return input_features
					def        UpperCAmelCase__ (							self							:	Optional[int]     , *UpperCAmelCase							:	Any     , **UpperCAmelCase							:	List[str]   )	->       int:
											return self.tokenizer.batch_decode(*UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[Any]     , *UpperCAmelCase							:	Dict     , **UpperCAmelCase							:	Any   )	->       Union[str, Any]:
											return self.tokenizer.decode(*UpperCAmelCase     , **UpperCAmelCase   )
					@contextmanager
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       int:
											warnings.warn(
											    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
											    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
											    'your audio inputs, or in a separate call.'   )
											lowerCAmelCase   :Dict						=			True
											lowerCAmelCase   :Optional[Any]						=			self.tokenizer
											yield
											lowerCAmelCase   :Optional[Any]						=			self.feature_extractor
											lowerCAmelCase   :List[Any]						=			False







"""simple docstring"""


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
		import onnxruntime as ort




@nightly
@require_onnxruntime
@require_torch_gpu
class 						__UpperCamelCase     (     unittest.TestCase						):
					@property
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Dict:
											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)
					@property
					def        UpperCAmelCase__ (							self							:	int   )	->       Optional[Any]:
											lowerCAmelCase   :Any						=			ort.SessionOptions()
											lowerCAmelCase   :Dict						=			False
											return options
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       str:
											lowerCAmelCase   :Union[str, Any]						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo.png'   )
											lowerCAmelCase   :str						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'   )
											lowerCAmelCase   :List[Any]						=			load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'   )

											# using the PNDM scheduler by default
											lowerCAmelCase   :Tuple						=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'     , revision='onnx'     , safety_checker=UpperCAmelCase     , feature_extractor=UpperCAmelCase     , provider=self.gpu_provider     , sess_options=self.gpu_options     , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase   )

											lowerCAmelCase   :Optional[Any]						=			'A red cat sitting on a park bench'

											lowerCAmelCase   :Optional[Any]						=			np.random.RandomState(0   )
											lowerCAmelCase   :Union[str, Any]						=			pipe(
											    prompt=UpperCAmelCase     , image=UpperCAmelCase     , mask_image=UpperCAmelCase     , strength=0.7_5     , guidance_scale=7.5     , num_inference_steps=15     , generator=UpperCAmelCase     , output_type='np'     , )

											lowerCAmelCase   :Optional[int]						=			output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image   ).max() < 1e-2